Hybrid Bit and Semantic Communications for UAV-Enabled Wireless Power Transfer Networks: A Decision-Assisted Deep Reinforcement Learning Approach

2026-05-30

AI Summary

This work addresses the challenge of jointly optimizing energy efficiency and spectral efficiency in unmanned aerial vehicle (UAV) networks constrained by limited energy resources and tight link budgets. To this end, the authors propose a multi-layer hybrid bit-semantic communication framework that, for the first time, deeply integrates semantic communication with wireless power transfer. The framework adaptively responds to dynamic channel conditions and task demands by jointly optimizing UAV trajectories, energy harvesting base station selection, user association, semantic encoding modes, and energy harvesting scheduling, augmented with a decision-aiding mechanism to enhance the convergence and long-term optimization capability of deep reinforcement learning. End-to-end training is performed using a distributed soft actor-critic (DSAC) algorithm. Simulation results demonstrate that the proposed approach significantly outperforms existing methods in dynamic environments, achieving substantial gains in both long-term semantic communication efficiency and overall system energy efficiency.

Abstract

Semantic communications which can significantly reduce spectrum consumption in wireless networks, have recently become a popular research area. When combined with wireless power transfer (WPT), semantic communications can help achieve high spectral efficiency for energy-limited devices in wireless communications. In energy-constrained and link budget-limited scenarios such as UAV networks, the integration of semantic communications and WPT enables highly energyefficient transmission mechanisms. In this paper, we investigate semantic communications in UAV-enabled WPT networks. To achieve adaptability to varying signal-to-noise ratio (SNR) and task requirements, we introduce a multi-layer hybrid bit and semantic communication framework. We adopt a semantic communication efficiency metric and aim to maximize it by jointly optimizing UAV trajectory, energy harvesting base station (EHBS) selection, user association, semantic mode selection, and energy harvesting time allocation. To address this complex longterm optimization problem, we introduce the distributional soft actor-critic (DSAC) algorithm and introduce a decision assistant to further enhance the convergence performance of DSAC. Simulation results validate the effectiveness of the proposed method and framework and demonstrate that our algorithm can achieve superior long-term optimization performance in dynamic network environments.

Problem

• semantic communications
• wireless power transfer
• UAV networks
• energy efficiency
• spectral efficiency

Innovation

• semantic communications
• wireless power transfer
• UAV networks
• deep reinforcement learning
• hybrid communication